Classification of Devnagari Numerals using Multiple Classifier
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2014 by IJCTT Journal | ||
| Volume-12 Number-4 |
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| Year of Publication : 2014 | ||
| Authors : Shashi Kant Shukla , Akhilesh Pandey | ||
| DOI : 10.14445/22312803/IJCTT-V12P139 |
Shashi Kant Shukla , Akhilesh Pandey."Classification of Devnagari Numerals using Multiple Classifier". International Journal of Computer Trends and Technology (IJCTT) V12(4):196-200, June 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
This paper presents a multiple classifier scheme for off-line hand written Devnagri numbers classification. The main purpose of this research is to find out best recognition result using multiple classifiers. This proposed technique uses simple profile and contour base triangular area representation technique for finding feature extraction and multiple classifier schemes on KNN, LDA, and KNN new neural network for classification. The performance of this technique has been tested with 36000 handwritten numerals randomly selected from CPAR datasets out of which 22000 datasets has been used for training sets and 14000 datasets has been used for test sets and we found the different result by different classifier
References
1. U. Pal, B.B. Chaudhri, “Indian Script character recognition: a survey Pattern Recognition”, The Journal of the Pattern Recognition Society, Available at ElsevierComputerScience.com, vol. 37, pp 1887-1899, Jun. 2004.
2. J. J. Hull, S.N. Srihari, E. Cahen, C. L. Kuan, P.Cullen and P. Palumbo, “A black-board approach to handwritten ZIP code recognition” in Proc. United States Postal Service Advance Technology Conf. 1988.
3. R. Plamondon and S. N. Srihari, “Online and Offline handwritten character recognition: A comprehensive survey”, IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 22, pp 62-84, 2000.
4. I. K. Sethi and B. Chatterjee, “Machine Recgnition of constrained Hand Printed Devengari”, Pattern Recognition, vol 9, pp 69-75, 1977.
5. I. K. Sethi and B. Chatterjee, “Machine Recgnition of handprinted Devengari numerals”, J. Inst. Electron. Telecommun. Eng. 22(1976), pp 532-535.
6. M. Hanmandlu, O. V. Ramana Murthy, “Fuzzy model based recognition of handwritten numerals”, Pattern Recognition, vol. 40, pp 1840-1854, 2007.
7. Reena Bajaj, Lipika Dey, and S. Chaudhury, “ Devnagari numerals recognition by combining decision of multiple connectionist classifiers”, Sadhana, col.27, part I pp-59-72, 2002.
8. U. Bhattacharya, B. B Chaudhri, R. Ghosh and M. Ghosh, “On Recognition of Handwritten Devengari numerals”,In proc. Of workshop on learning Algorithms for Pattern recognition, Sydeny, pp 1-7 2005.
9. R.M.K Sinha, H.N. Mahabala, “Machine recognition of Devnagari script”, IEEE Trans. Syst Man Cybern 9, pp. 435-441, 1979.
10. I.K.Sethi, B.Chatterjee, “Machine recognition of handprinted Devnagari numerals”, J Inst, Electron. Telecommun. Eng 22, pp. 532-535, 1976.
11. R. Bajaj, L. Dey, S. Chaudhury, “Devnagari numeral recognition by combining decision of multiple connectionist classifiers”,Sadhana 27-1, pp. 59-72, 2002.
12. V. Bansal, R.M.K. Sinha, “A Devanagari OCR and a brief review of OCR research for Indian scripts.” Proceedings of STRANS01, IIT, Kanpur, India, 2001
13. S. Khedekar, V. Ramanaprasad, S. Setlur, V. Govindaraju, “Text- image sepration in devanagari documents”, Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR’03), Edinburgh, Scotland, 3-6 August, 2003, pp 1265-1269.
14. B.B. Chaudhuri, U. Pal “An OCR system to read two Indian language scripts: Bangla and Devanagari (Devnagri”), in: Proceedings of fourth IEEE International Conference on Document Analysis and Recognition (ICDAR’97), Ulm. Germany, August 18-20, 1997, pp. 1011-1015.
15. S. Antanani, L. Agnihotri, “Gujarati character recognition”, in: Proceeding of fifth IEEE International conference on document Analysis and Recognition (ICDAR’99), Banglore, India, 20-22 September, 1999, pp. 418-421.
16. M. Hanmandlu, K.R Murali Mohan, H.Kumar, “Neural based handwritten character recognition”, in Proceeding of fifth IEEE International Conference, pp 241-244, 1999.
17. R.M.K. Sinha, H.N. Mahabala, “Machine recognition of Devanagari script”, IEEE Trans. Syst. Man Cybern 9(8) (1979), pp 435-441.
18. J.C Sant, S,K, Mulick, “Handwritten Devanagari script recognition using CTNNSE algorithm”, International Conference on Application of Information Technology in south Asia language, AKSHARA’94, NewDelhi, India, pp. 25-26.
19. A. Elnagar, S. Harous, “Recognition of Handwritten Devnagri Numerals using structural descriptors”, J. Exp. Thor. Artif. Intell. 15(3), 2003, pp 299-314.
20. C.L. Liu, K. Nakashima, H. Sako, H.Fujisawa, “Handwritten digit recognition: benchmarking of state-of-the-art techniques”, Pattern Recognition 36(10), 2003, pp 2271-2285.
21. K.Y. Rajput and Sangeeta Mishra, “Recognition and Editing of Devnagari Handwriting Using Neural Network”, Proceedings of SPIT-IEEE Colloquium and International Conference, Mumbai, India, Vol. 1,66.
22. Anil k. Jain, Jianchang Mao, K.M. Mohiuddin, “Artifical Neural Network: A Tutorial”, IEEE 0018-9162/96, March 1996.
23. Y. Lu and M. Shridhar, “Character Segmentation in Handwritten Words – An Overview”, Pattern Recognition”,Vol. 29, 1996, pp. 77-96.
24. R.M.K. Sinha and V. Bansal, “On Devanagari document processing,” Proc. Int. Conf. on Systems, Man and Cybernetics” , Vancouver, BC, pp. 1621-1626,Oct. 1995.
25. R.M.K.Sinha, “Rule based contextual post-processing for Devanagari text recognition” , Pattern Recognition, 20(5), pp. 475-485, 1987.
26. S. N. Srihari, “Recognition of Handwritten and Machineprinted Text for Postal Address Interpretation”, Pattern Recognition Letters, 14, 1993, pp. 291-302.
27. U. Bhattacharya, B. B. Chaudhuri, “ A multiple classifier scheme for multiresolution recognition of handprinted numerals”, Proceeding of seventh International Conference on Document Analysis and Recognition, 2003.
Keywords
Multiple classifier schemes, Multiple Feature extraction.